Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity

12 October 2018

Papers citing "Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity"

28 / 28 papers shown

Title
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance Dimitris Oikonomou Nicolas Loizou 55 4 0 06 Jun 2024
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation Aaron Mishkin Mert Pilanci Mark Schmidt 64 1 0 03 Apr 2024
Eva: A General Vectorized Approximation Framework for Second-order Optimization Lin Zhang S. Shi Bo-wen Li 28 1 0 04 Aug 2023
MoMo: Momentum Models for Adaptive Learning Rates Fabian Schaipp Ruben Ohana Michael Eickenberg Aaron Defazio Robert Mansel Gower 32 10 0 12 May 2023
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance Krishnakumar Balasubramanian Promit Ghosal Ye He 35 5 0 03 Apr 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 25 2 0 20 Feb 2023
Asymptotic normality and optimality in nonsmooth stochastic approximation Damek Davis Dmitriy Drusvyatskiy L. Jiang 18 13 0 16 Jan 2023
A Stochastic Proximal Polyak Step Size Fabian Schaipp Robert Mansel Gower M. Ulbrich 14 12 0 12 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 34 2 0 09 Jan 2023
Private optimization in the interpolation regime: faster rates and hardness results Hilal Asi Karan N. Chadha Gary Cheng John C. Duchi 47 5 0 31 Oct 2022
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method A. Wigren Johan Wågberg Fredrik Lindsten A. Wills Thomas B. Schon 24 10 0 26 Oct 2022
The Stochastic Proximal Distance Algorithm Hao Jiang Jason Xu 30 0 0 21 Oct 2022
Augmented Lagrangian Methods for Time-varying Constrained Online Convex Optimization Haoyang Liu X. Xiao Liwei Zhang 16 4 0 19 May 2022
Making SGD Parameter-Free Y. Carmon Oliver Hinder 25 41 0 04 May 2022
Amortized Proximal Optimization Juhan Bae Paul Vicol Jeff Z. HaoChen Roger C. Grosse ODL 25 14 0 28 Feb 2022
Parameter-free Mirror Descent Andrew Jacobsen Ashok Cutkosky 17 32 0 26 Feb 2022
Understanding AdamW through Proximal Methods and Scale-Freeness Zhenxun Zhuang Mingrui Liu Ashok Cutkosky Francesco Orabona 39 63 0 31 Jan 2022
A Stochastic Bundle Method for Interpolating Networks Alasdair Paren Leonard Berrada Rudra P. K. Poudel M. P. Kumar 24 4 0 29 Jan 2022
Mitigating Divergence of Latent Factors via Dual Ascent for Low Latency Event Prediction Models A. Shtoff Yair Koren 14 0 0 15 Nov 2021
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum J. Kim Panos Toulis Anastasios Kyrillidis 24 8 0 11 Nov 2021
Private Adaptive Gradient Methods for Convex Optimization Hilal Asi John C. Duchi Alireza Fallah O. Javidbakht Kunal Talwar 11 53 0 25 Jun 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 29 17 0 22 Jun 2021
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness Vien V. Mai M. Johansson 31 38 0 12 Feb 2021
Practical Precoding via Asynchronous Stochastic Successive Convex Approximation Basil M. Idrees J. Akhtar K. Rajawat 16 6 0 03 Oct 2020
Complexity Guarantees for Polyak Steps with Momentum Mathieu Barré Adrien B. Taylor Alexandre d’Aspremont 22 26 0 03 Feb 2020
Stochastic quasi-Newton with line-search regularization A. Wills Thomas B. Schon ODL 16 21 0 03 Sep 2019
Protection Against Reconstruction and Its Applications in Private Federated Learning Abhishek Bhowmick John C. Duchi Julien Freudiger Gaurav Kapoor Ryan M. Rogers FedML 16 357 0 03 Dec 2018
Learning without Concentration S. Mendelson 85 333 0 01 Jan 2014